Overview

Dataset statistics

Number of variables62
Number of observations663
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory135.4 KiB
Average record size in memory209.2 B

Variable types

BOOL53
NUM8
CAT1

Warnings

importacia_salud_fisica has 19 (2.9%) zeros Zeros
importancia_salud_mental has 45 (6.8%) zeros Zeros
empresa_anterior_importancia_salud_fisica has 37 (5.6%) zeros Zeros
empresa_anterior_importancia_salud_mental has 102 (15.4%) zeros Zeros
hablar_amigos_familia_EM has 19 (2.9%) zeros Zeros
reaccion_companeros_EM has 18 (2.7%) zeros Zeros

Reproduction

Analysis started2020-10-21 07:44:57.056968
Analysis finished2020-10-21 07:45:47.883685
Duration50.83 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
365 
0
298 
ValueCountFrequency (%) 
136555.1%
 
029844.9%
 
2020-10-21T02:45:47.919691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
440 
1
223 
ValueCountFrequency (%) 
044066.4%
 
122333.6%
 
2020-10-21T02:45:48.046691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
371 
1
292 
ValueCountFrequency (%) 
037156.0%
 
129244.0%
 
2020-10-21T02:45:48.153687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
343 
1
320 
ValueCountFrequency (%) 
034351.7%
 
132048.3%
 
2020-10-21T02:45:48.247710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

importacia_salud_fisica
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.208144796
Minimum0
Maximum10
Zeros19
Zeros (%)2.9%
Memory size5.2 KiB
2020-10-21T02:45:48.426755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.321343247
Coefficient of variation (CV)0.3739189923
Kurtosis0.1807914215
Mean6.208144796
Median Absolute Deviation (MAD)1
Skewness-0.6331851862
Sum4116
Variance5.388634468
MonotocityNot monotonic
2020-10-21T02:45:48.720686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
714221.4%
 
512118.3%
 
811016.6%
 
68112.2%
 
9477.1%
 
10456.8%
 
3375.6%
 
4274.1%
 
2253.8%
 
0192.9%
 
ValueCountFrequency (%) 
0192.9%
 
191.4%
 
2253.8%
 
3375.6%
 
4274.1%
 
ValueCountFrequency (%) 
10456.8%
 
9477.1%
 
811016.6%
 
714221.4%
 
68112.2%
 

importancia_salud_mental
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.843137255
Minimum0
Maximum10
Zeros45
Zeros (%)6.8%
Memory size5.2 KiB
2020-10-21T02:45:48.965686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.443218086
Coefficient of variation (CV)0.504470131
Kurtosis-0.4671906354
Mean4.843137255
Median Absolute Deviation (MAD)2
Skewness-0.1738910157
Sum3211
Variance5.969314614
MonotocityNot monotonic
2020-10-21T02:45:49.158725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
516625.0%
 
78412.7%
 
37411.2%
 
66710.1%
 
8609.0%
 
4598.9%
 
2487.2%
 
0456.8%
 
1253.8%
 
9182.7%
 
ValueCountFrequency (%) 
0456.8%
 
1253.8%
 
2487.2%
 
37411.2%
 
4598.9%
 
ValueCountFrequency (%) 
10172.6%
 
9182.7%
 
8609.0%
 
78412.7%
 
66710.1%
 
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
582 
0
81 
ValueCountFrequency (%) 
158287.8%
 
08112.2%
 
2020-10-21T02:45:49.314687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
473 
0
190 
ValueCountFrequency (%) 
147371.3%
 
019028.7%
 
2020-10-21T02:45:49.418686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
525 
1
138 
ValueCountFrequency (%) 
052579.2%
 
113820.8%
 
2020-10-21T02:45:49.520684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
466 
1
197 
ValueCountFrequency (%) 
046670.3%
 
119729.7%
 
2020-10-21T02:45:49.627690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
478 
1
185 
ValueCountFrequency (%) 
047872.1%
 
118527.9%
 
2020-10-21T02:45:49.730722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.423831071
Minimum0
Maximum10
Zeros37
Zeros (%)5.6%
Memory size5.2 KiB
2020-10-21T02:45:49.908761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.306533302
Coefficient of variation (CV)0.4252590599
Kurtosis0.1714301966
Mean5.423831071
Median Absolute Deviation (MAD)2
Skewness-0.451859016
Sum3596
Variance5.320095875
MonotocityNot monotonic
2020-10-21T02:45:50.159688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
522133.3%
 
79514.3%
 
88012.1%
 
67010.6%
 
3395.9%
 
0375.6%
 
4375.6%
 
2274.1%
 
9243.6%
 
10223.3%
 
ValueCountFrequency (%) 
0375.6%
 
1111.7%
 
2274.1%
 
3395.9%
 
4375.6%
 
ValueCountFrequency (%) 
10223.3%
 
9243.6%
 
88012.1%
 
79514.3%
 
67010.6%
 
Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.687782805
Minimum0
Maximum10
Zeros102
Zeros (%)15.4%
Memory size5.2 KiB
2020-10-21T02:45:50.403686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.36041468
Coefficient of variation (CV)0.6400633673
Kurtosis-0.5879729007
Mean3.687782805
Median Absolute Deviation (MAD)1
Skewness0.01220034736
Sum2445
Variance5.571557463
MonotocityNot monotonic
2020-10-21T02:45:50.685685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
519529.4%
 
010215.4%
 
38112.2%
 
26910.4%
 
4609.0%
 
6477.1%
 
1416.2%
 
7365.4%
 
8213.2%
 
1071.1%
 
ValueCountFrequency (%) 
010215.4%
 
1416.2%
 
26910.4%
 
38112.2%
 
4609.0%
 
ValueCountFrequency (%) 
1071.1%
 
940.6%
 
8213.2%
 
7365.4%
 
6477.1%
 

tiene_EM
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
458 
0
205 
ValueCountFrequency (%) 
145869.1%
 
020530.9%
 
2020-10-21T02:45:50.910689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

hablar_amigos_familia_EM
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.644042232
Minimum0
Maximum10
Zeros19
Zeros (%)2.9%
Memory size5.2 KiB
2020-10-21T02:45:51.046730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.704475972
Coefficient of variation (CV)0.4070527967
Kurtosis-0.3864312595
Mean6.644042232
Median Absolute Deviation (MAD)2
Skewness-0.6785971221
Sum4405
Variance7.314190282
MonotocityNot monotonic
2020-10-21T02:45:51.235688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
811417.2%
 
1010816.3%
 
79914.9%
 
97911.9%
 
57110.7%
 
6568.4%
 
2395.9%
 
3365.4%
 
4304.5%
 
0192.9%
 
ValueCountFrequency (%) 
0192.9%
 
1121.8%
 
2395.9%
 
3365.4%
 
4304.5%
 
ValueCountFrequency (%) 
1010816.3%
 
97911.9%
 
811417.2%
 
79914.9%
 
6568.4%
 
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
583 
1
80 
ValueCountFrequency (%) 
058387.9%
 
18012.1%
 
2020-10-21T02:45:51.382686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

reaccion_companeros_EM
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.39668175
Minimum0
Maximum10
Zeros18
Zeros (%)2.7%
Memory size5.2 KiB
2020-10-21T02:45:51.529736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.22613992
Coefficient of variation (CV)0.4125016117
Kurtosis-0.1025746685
Mean5.39668175
Median Absolute Deviation (MAD)1
Skewness-0.09538718386
Sum3578
Variance4.955698942
MonotocityNot monotonic
2020-10-21T02:45:51.726722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
516224.4%
 
69213.9%
 
78713.1%
 
48012.1%
 
86710.1%
 
3619.2%
 
2345.1%
 
10304.5%
 
9213.2%
 
0182.7%
 
ValueCountFrequency (%) 
0182.7%
 
1111.7%
 
2345.1%
 
3619.2%
 
48012.1%
 
ValueCountFrequency (%) 
10304.5%
 
9213.2%
 
86710.1%
 
78713.1%
 
69213.9%
 

apoyo_industria_tecnologia_EM
Real number (ℝ≥0)

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.544494721
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-10-21T02:45:51.935686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8934449145
Coefficient of variation (CV)0.3511286179
Kurtosis-0.5138950559
Mean2.544494721
Median Absolute Deviation (MAD)1
Skewness-0.04508430153
Sum1687
Variance0.7982438153
MonotocityNot monotonic
2020-10-21T02:45:52.112685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
327341.2%
 
221632.6%
 
18813.3%
 
48212.4%
 
540.6%
 
ValueCountFrequency (%) 
18813.3%
 
221632.6%
 
327341.2%
 
48212.4%
 
540.6%
 
ValueCountFrequency (%) 
540.6%
 
48212.4%
 
327341.2%
 
221632.6%
 
18813.3%
 
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
407 
1
256 
ValueCountFrequency (%) 
040761.4%
 
125638.6%
 
2020-10-21T02:45:52.271720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

edad
Real number (ℝ≥0)

Distinct45
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.3453997
Minimum19
Maximum66
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-10-21T02:45:52.430722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile24
Q129
median35
Q340
95-th percentile50
Maximum66
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.156874241
Coefficient of variation (CV)0.2307761211
Kurtosis0.4941711857
Mean35.3453997
Median Absolute Deviation (MAD)6
Skewness0.6784032192
Sum23434
Variance66.53459739
MonotocityNot monotonic
2020-10-21T02:45:52.670696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
35395.9%
 
37385.7%
 
30385.7%
 
28355.3%
 
32335.0%
 
33324.8%
 
29314.7%
 
34284.2%
 
31284.2%
 
38274.1%
 
Other values (35)33450.4%
 
ValueCountFrequency (%) 
1920.3%
 
2030.5%
 
2150.8%
 
22111.7%
 
23101.5%
 
ValueCountFrequency (%) 
6610.2%
 
6410.2%
 
6210.2%
 
6130.5%
 
6010.2%
 

genero
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
443 
0
207 
2
 
13
ValueCountFrequency (%) 
144366.8%
 
020731.2%
 
2132.0%
 
2020-10-21T02:45:52.920690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-21T02:45:53.058686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:53.248690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
487 
1
176 
ValueCountFrequency (%) 
048773.5%
 
117626.5%
 
2020-10-21T02:45:53.392728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
552 
1
111 
ValueCountFrequency (%) 
055283.3%
 
111116.7%
 
2020-10-21T02:45:53.480687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
606 
1
 
57
ValueCountFrequency (%) 
060691.4%
 
1578.6%
 
2020-10-21T02:45:53.798726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
574 
1
89 
ValueCountFrequency (%) 
057486.6%
 
18913.4%
 
2020-10-21T02:45:53.893741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
450 
1
213 
ValueCountFrequency (%) 
045067.9%
 
121332.1%
 
2020-10-21T02:45:53.983727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
607 
1
 
56
ValueCountFrequency (%) 
060791.6%
 
1568.4%
 
2020-10-21T02:45:54.081690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
643 
1
 
20
ValueCountFrequency (%) 
064397.0%
 
1203.0%
 
2020-10-21T02:45:54.179722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
1
418 
0
245 
ValueCountFrequency (%) 
141863.0%
 
024537.0%
 
2020-10-21T02:45:54.272686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
402 
1
261 
ValueCountFrequency (%) 
040260.6%
 
126139.4%
 
2020-10-21T02:45:54.368713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
445 
1
218 
ValueCountFrequency (%) 
044567.1%
 
121832.9%
 
2020-10-21T02:45:54.463727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
544 
1
119 
ValueCountFrequency (%) 
054482.1%
 
111917.9%
 
2020-10-21T02:45:54.562689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
580 
1
83 
ValueCountFrequency (%) 
058087.5%
 
18312.5%
 
2020-10-21T02:45:54.659690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
583 
1
80 
ValueCountFrequency (%) 
058387.9%
 
18012.1%
 
2020-10-21T02:45:54.761723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
475 
1
188 
ValueCountFrequency (%) 
047571.6%
 
118828.4%
 
2020-10-21T02:45:54.857702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
534 
1
129 
ValueCountFrequency (%) 
053480.5%
 
112919.5%
 
2020-10-21T02:45:54.948728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
558 
1
105 
ValueCountFrequency (%) 
055884.2%
 
110515.8%
 
2020-10-21T02:45:55.048723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
345 
1
318 
ValueCountFrequency (%) 
034552.0%
 
131848.0%
 
2020-10-21T02:45:55.146725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
550 
1
113 
ValueCountFrequency (%) 
055083.0%
 
111317.0%
 
2020-10-21T02:45:55.245728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
460 
1
203 
ValueCountFrequency (%) 
046069.4%
 
120330.6%
 
2020-10-21T02:45:55.341727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
1
386 
0
277 
ValueCountFrequency (%) 
138658.2%
 
027741.8%
 
2020-10-21T02:45:55.438686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
623 
1
 
40
ValueCountFrequency (%) 
062394.0%
 
1406.0%
 
2020-10-21T02:45:55.536732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
504 
1
159 
ValueCountFrequency (%) 
050476.0%
 
115924.0%
 
2020-10-21T02:45:55.628722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
1
346 
0
317 
ValueCountFrequency (%) 
134652.2%
 
031747.8%
 
2020-10-21T02:45:55.726686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
443 
1
220 
ValueCountFrequency (%) 
044366.8%
 
122033.2%
 
2020-10-21T02:45:55.829685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
626 
1
 
37
ValueCountFrequency (%) 
062694.4%
 
1375.6%
 
2020-10-21T02:45:55.948690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
462 
1
201 
ValueCountFrequency (%) 
046269.7%
 
120130.3%
 
2020-10-21T02:45:56.108686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
477 
1
186 
ValueCountFrequency (%) 
047771.9%
 
118628.1%
 
2020-10-21T02:45:56.251689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
474 
1
189 
ValueCountFrequency (%) 
047471.5%
 
118928.5%
 
2020-10-21T02:45:56.381689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
358 
1
305 
ValueCountFrequency (%) 
035854.0%
 
130546.0%
 
2020-10-21T02:45:56.519689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
631 
1
 
32
ValueCountFrequency (%) 
063195.2%
 
1324.8%
 
2020-10-21T02:45:56.610686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
529 
1
134 
ValueCountFrequency (%) 
052979.8%
 
113420.2%
 
2020-10-21T02:45:56.697688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
1
466 
0
197 
ValueCountFrequency (%) 
146670.3%
 
019729.7%
 
2020-10-21T02:45:56.807725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
563 
1
100 
ValueCountFrequency (%) 
056384.9%
 
110015.1%
 
2020-10-21T02:45:56.897729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
1
474 
0
189 
ValueCountFrequency (%) 
147471.5%
 
018928.5%
 
2020-10-21T02:45:56.993686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
635 
1
 
28
ValueCountFrequency (%) 
063595.8%
 
1284.2%
 
2020-10-21T02:45:57.094690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
372 
1
291 
ValueCountFrequency (%) 
037256.1%
 
129143.9%
 
2020-10-21T02:45:57.192690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
584 
1
79 
ValueCountFrequency (%) 
058488.1%
 
17911.9%
 
2020-10-21T02:45:57.290689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
539 
1
124 
ValueCountFrequency (%) 
053981.3%
 
112418.7%
 
2020-10-21T02:45:57.428688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
429 
1
234 
ValueCountFrequency (%) 
042964.7%
 
123435.3%
 
2020-10-21T02:45:57.538692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
518 
1
145 
ValueCountFrequency (%) 
051878.1%
 
114521.9%
 
2020-10-21T02:45:57.644684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size663.0 B
0
537 
1
126 
ValueCountFrequency (%) 
053781.0%
 
112619.0%
 
2020-10-21T02:45:57.747689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-10-21T02:45:09.469901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:09.724942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:09.968898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:10.369902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:10.752938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:10.964898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:11.265898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:11.542899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:11.842904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:12.134919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:12.448896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:12.695936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:12.935899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:13.170903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:13.399938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:13.658982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:13.883939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:14.113897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:14.346937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:14.582940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:14.830939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:15.054930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:15.288985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:15.560933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:15.792188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:16.018944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:16.252915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:16.483902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:16.715939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:17.092899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:17.374800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:17.684904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:18.012896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:18.226734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:18.460694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:18.704695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:18.958691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:19.272692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:19.573691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:19.924694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:20.142149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:20.370113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:20.594404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:20.838385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:21.062421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:21.302692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:21.533708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:21.806687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:22.020686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:22.276689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:22.541724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:22.820728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:23.090723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:23.356686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:23.608723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:23.907686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:24.155722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:24.372732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:24.590725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:24.819724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:25.041726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:25.253725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:25.467687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:25.708724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-21T02:45:58.053692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-21T02:46:04.350688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-21T02:46:10.609726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-21T02:46:16.865706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-21T02:45:26.579696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-21T02:45:43.425725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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Last rows

concimiento_cubrimiento_EMhabla_jefe_EM_flaghablar_companeros_EM_flaghablar_companeros_EM_conmigoimportacia_salud_fisicaimportancia_salud_mentaltrabajado_otras_empresaempresa_anterior_tecnologiaempresa_anterior_hablar_jefe_EM_flagempresa_anterior_hablar_companeros_EMempresa_anterior_hablar_companeros_conmigoempresa_anterior_importancia_salud_fisicaempresa_anterior_importancia_salud_mentaltiene_EMhablar_amigos_familia_EMidentificado_EM_trabajoreaccion_companeros_EMapoyo_industria_tecnologia_EMdispuesto_contar_experienciaedadgeneroNo_empleados_100-500No_empleados_26-100No_empleados_500-1000No_empleados_6-25No_empleados_More than 1000cubrimiento_EM_Nocubrimiento_EM_Not eligible for coverage / NAcubrimiento_EM_Yesrecursos_EM_Norecursos_EM_Yesdejar_trabajo_EM_I don't knowdejar_trabajo_EM_Neither easy nor difficultdejar_trabajo_EM_Somewhat difficultdejar_trabajo_EM_Somewhat easydejar_trabajo_EM_Very easyempresa_anterior_cargo_tecnologia_No, none didempresa_anterior_cargo_tecnologia_Some didempresa_anterior_cargo_tecnologia_Yes, they all didempresa_anterior_hablar_jefe_EM_No, none of my previous supervisorsempresa_anterior_hablar_jefe_EM_Some of my previous supervisorsempresa_anterior_hablar_jefe_EM_Yes, all of my previous supervisorsEM_familiar_NoEM_familiar_YesEM_interfiere_trabajo_buen_tratamiento_Not applicable to meEM_interfiere_trabajo_buen_tratamiento_OftenEM_interfiere_trabajo_buen_tratamiento_RarelyEM_interfiere_trabajo_buen_tratamiento_SometimesEM_interfiere_trabajo_mal_tratamiento_Not applicable to meEM_interfiere_trabajo_mal_tratamiento_OftenEM_interfiere_trabajo_mal_tratamiento_RarelyEM_interfiere_trabajo_mal_tratamiento_Sometimesmiedo_hablar_EM_Nomiedo_hablar_EM_Yesentrevista_trabajo_hablar_EM_Noentrevista_trabajo_hablar_EM_Yesno_apoyo_EM_Nono_apoyo_EM_Yes, I experiencedno_apoyo_EM_Yes, I observedapoyo_EM_Noapoyo_EM_Yes, I experiencedapoyo_EM_Yes, I observed
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